RRepoGEO

REPOGEO REPORT · LITE

going-doer/Paper2Code

Default branch master · commit ba916997 · scanned 5/20/2026, 2:33:35 PM

GitHub: 4,619 stars · 656 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface going-doer/Paper2Code, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify Paper2Code's unique system approach in the README's opening.

    Why:

    CURRENT
    PaperCoder is the multi-agent LLM system introduced in Paper2Code, designed to transform a paper into a code repository. It follows a three-stage pipeline: planning, analysis, and code generation, each handled by specialized agents. Our method outperforms strong baselines on both Paper2Code and PaperBench and produces faithful, high-quality implementations.
    COPY-PASTE FIX
    Paper2Code is a pioneering **multi-agent LLM system** designed to fully automate the transformation of scientific papers into high-quality, runnable code repositories. It uniquely employs a sophisticated three-stage pipeline—planning, analysis, and code generation—with specialized agents, setting it apart from general-purpose LLMs or development tools.
  • mediumreadme#2
    Add a "Comparison with Existing Tools" section to the README.

    Why:

    COPY-PASTE FIX
    ## 🆚 Comparison with Existing Tools

Paper2Code stands apart from general-purpose LLMs like GPT-4 or Llama 2, which require extensive prompting and manual integration for code generation. Unlike development environments such as Jupyter Notebooks or GitHub Copilot, Paper2Code offers an end-to-end, automated multi-agent system specifically designed to translate entire scientific papers into runnable code repositories, significantly reducing the manual effort in reproducibility and implementation.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface going-doer/Paper2Code
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 2×
  2. Jupyter Notebooks / JupyterLab · recommended 1×
  3. GitHub · recommended 1×
  4. GitLab · recommended 1×
  5. Hugging Face · recommended 1×
  • CATEGORY QUERY
    How can I automatically convert research papers into runnable code implementations?
    you: not recommended
    AI recommended (in order):
    1. Jupyter Notebooks / JupyterLab
    2. GitHub
    3. GitLab
    4. Hugging Face
    5. pylatexenc
    6. texsoup
    7. SymPy
    8. Mathematica
    9. MATLAB's Symbolic Math Toolbox
    10. ANTLR
    11. PLY
    12. GPT-4
    13. Claude 3
    14. Llama 3
    15. OpenCV
    16. Tesseract OCR

    AI recommended 16 alternatives but never named going-doer/Paper2Code. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist to generate machine learning code directly from academic publications?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT
    2. GPT-4
    3. Claude
    4. Llama 2
    5. GitHub Copilot
    6. Google Gemini Code Assist
    7. Jupyter AI
    8. Google Colab
    9. Semantic Scholar API
    10. ArXiv API
    11. Hugging Face Transformers
    12. PyTorch Lightning
    13. Keras

    AI recommended 13 alternatives but never named going-doer/Paper2Code. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of going-doer/Paper2Code?
    pass
    AI named going-doer/Paper2Code explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts going-doer/Paper2Code in production, what risks or prerequisites should they evaluate first?
    pass
    AI named going-doer/Paper2Code explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo going-doer/Paper2Code solve, and who is the primary audience?
    pass
    AI named going-doer/Paper2Code explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of going-doer/Paper2Code. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/going-doer/Paper2Code.svg)](https://repogeo.com/en/r/going-doer/Paper2Code)
HTML
<a href="https://repogeo.com/en/r/going-doer/Paper2Code"><img src="https://repogeo.com/badge/going-doer/Paper2Code.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

going-doer/Paper2Code — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite